关键词: 钉螺, 密度, 气候, 土地利用, 景观格局, 交互效应, 广义可加模型, 云南省

Abstract: Objective　To examine the impact of climate, land use and their interaction effect on the population density of Oncomelania hupensis. Methods　O. hupensis snail surveillance data in Yunnan Province from 2013 to 2022 were obtained from the National Information System for Parasitic Diseases Prevention and Control of Chinese Information System for Disease Control and Prevention, and the density of living snails was calculated in each county⁃level administrative district. Climate data were obtained from the China Meteorological Forcing Dataset version 2.0, and land use data were captured from the China Land Cover Dataset. The raster climate data and land use data were clipped to the administrative border of Yunnan Province in the R package version 4.5, and the climate data were resampled to a spatial resolution of 30 m × 30 m consistent with the land use data using bilinear interpolation. Annual meteorological indicators were extracted from each county⁃level schistosomiasis surveillance administrative area in Yunnan Province from 2013 to 2022, including average temperature, maximum temperature, minimum temperature, temperature difference, average precipitation rate, maximum precipitation rate, minimum precipitation rate, downward shortwave radiation flux, downward longwave radiation flux, air pressure, wind speed, specific humidity and relative humidity. Annual land use structure and landscape pattern index in each county⁃level schistosomiasis surveillance administrative area in Yunnan Province from 2013 to 2022 were extracted and calculated, including the proportion of areas occupied by each type of land use, patch density, largest patch index, landscape separation index, landscape fragmentation index, landscape segmentation index, contagion index, patch cohesion index, edge density, and shape index. Land use entropy index was introduced to represent the degree of habitat fragmentation and ecological niche breadth. The density of living snails and environmental factors were subjected to rank correlation analysis, and variables with no statistically significant correlation (P > 0.1) were excluded. Then, a correlation coefficient matrix was constructed, and ∣rs∣ of 0.85 and greater was defined as the threshold of high collinearity. For highly correlated variables in each group, indicators with a more biological explanatory power or a better statistical efficiency were retained. A generalized additive model (GAM) was constructed with O. hupensis snail density as a response variable and screened environmental variables as independent variables, and the model performance was evaluated using the variance explained. Results　The area occupied by O. hupensis snail habitats was 1 056 to 1 680 hm2 in Yunnan Province from 2013 to 2022, appearing an overall tendency towards a decline; however, the density of living snails appeared a tendency towards a slight rise. Based on the correlation coefficient matrix, 14 core explanatory variables were retained, including farmland, water bodies, bare land, impervious surface, landscape separation index, landscape fragmentation index, edge density, shape index, land use entropy index, downward longwave radiation flux, downward shortwave radiation flux, maximum temperature, minimum precipitation rate and specific humidity. Two types of GAM were constructed, including the main⁃effects model without interaction terms and the interaction model with interaction terms. The corrected Akaike information criterion (AICc) of the optimal main⁃ effects model was -285.223, and the core variables included bare land, impervious surface, landscape separation index, edge density, shape index, land use entropy index and downward shortwave radiation flux. Following introduction of second⁃order interaction terms, the AICc value of the optimal interaction model was -345.526, and the significant interaction combinations included: farmland × impervious surface, farmland × landscape separation index, farmland × shape index, farmland × land use entropy index, impervious surface × landscape separation index, impervious surface × edge density, and shape index × downward shortwave radiation flux. The proportion of the main⁃effects model explaining the variation of O. hupensis snail density was 39.8%, and the explanatory power of GAM increased to 79.0% following introduction of interaction terms. Conclusion　Landscape separation index, edge density, shape index and other landscape pattern indicators pose significant impacts on the intensity and direction of changes in O. hupensis snail density in Yunnan Province.

Key words: Oncomelania hupensis, Density, Climate, Land use, Landscape pattern, Interaction effect, Generalized additive model, Yunnan Province